Context Familial neurohypophyseal diabetes insipidus is a rare disease produced by a deficiency in the secretion of antidiuretic hormone and is caused by mutations in the arginine vasopressin gene. Objective Clinical, biochemical, and genetic characterization of a group of patients clinically diagnosed with familial neurohypophyseal diabetes insipidus, 1 of the largest cohorts of patients with protein neurophysin II (AVP-NPII) gene alterations studied so far. Design The AVP-NPII gene was screened for mutations by PCR followed by direct Sanger sequencing in 15 different unrelated families from Spain. Results The 15 probands presented with polyuria and polydipsia as the most important symptoms at the time of diagnosis. In these patients, the disease was diagnosed at a median of 6 years of age. We observed 11 likely pathogenic variants. Importantly, 4 of the AVP-NPII variants were novel (p.(Tyr21Cys), p.(Gly45Ser), p.(Cys75Tyr), p.(Gly88Cys)). Conclusions Cytotoxicity seems to be due to consequences common to all the variants found in our cohort, which are not able to fold correctly and pass the quality control of the ER. In concordance, we found autosomal dominant familial neurohypophyseal diabetes insipidus in the 15 families studied.
Hypopituitarism is a rare disorder. Hypopituitarism can present as a deficiency of individual anterior pituitary hormones (e.g., adrenocorticotropic hormone, thyroid-stimulating hormone, luteinizing hormone, follicle-stimulating hormone, prolactin, growth hormone) or posterior pituitary hormones (e.g., oxytocin, vasopressin) or as the deficiency of all these pituitary hormones, also known as panhypopituitarism. Here, we discuss a 59-year-old man who presented with two episodes of unwitnessed syncope after an episode of vomiting. On admission, the patient was hypotensive to 88/54 mmHg, afebrile, and with a leukocyte count of 21.43 K/µL (reference range: 3.80 to 10.50 K/µL). CT scan of the head revealed a hyperdensity in the left intracranial internal carotid artery just proximal to the bifurcation, suggesting an artifact or presence of an embolus. Additional findings included a sellar mass with calcifications and suprasellar extensions. The patient was admitted for further workup of syncope. Other differential diagnoses included sepsis, stroke, cardiac arrhythmias, and pulmonary embolism. Sepsis, stroke, and cardiac workup were negative for significant findings. The patient remained persistently hypotensive despite aggressive intravenous hydration, raising suspicion for an underlying endocrine disorder. MRI of the brain was negative for stroke but again was significant for a sellar mass. Additional workup showed a deficiency of all the anterior pituitary hormones likely secondary to mass effect. The patient was diagnosed with panhypopituitarism due to pituitary macroadenoma.
The Centers for Disease Control and Prevention (CDC) diabetes prevention program (DPP) has formed the foundation
for Type 2 Diabetes Mellitus (T2DM) prevention efforts and lifestyle change modifications in multiple care settings. To our knowledge, no randomized controlled trial has verified the efficacy of a fully mobile version of CDC’s diabetes prevention program (DPP). This study aimed to investigate the long-term weight loss and glycemic efficacy of a mobile-delivered DPP compared with a control group receiving usual medical care. Adults with prediabetes (N=202) were recruited from a clinic and randomized to either a mobile-delivered, coach-guided DPP (Noom) or a control group that received regular medical care including a paper-based DPP curriculum and no formal intervention. The intervention group learned how to use the Noom program, how to interact with their coach, and the importance of maintaining motivation. They had access to an interactive coach-to-participant interface and group messaging, daily challenges for behavior change, DPP-based education articles, food logging, and automated feedback. Primary outcomes included changes in weight and hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) levels at 6 and 12 months, respectively. Exploratory secondary outcomes included program engagement as a predictor of changes in weight and HbA<sub>1c</sub> levels. A total of 202 participants were recruited and randomized into the intervention (n=101) or control group (n=99). In the intention-to-treat (ITT) analyses, changes in the participants’ weight and BMI were significantly different at 6 months between the intervention and control groups, but there was no difference in HbA<sub>1c</sub> levels (mean difference 0.004%, SE 0.05; <i>P</i>=.94). Weight and BMI were lower in the intervention group by −2.64 kg (SE 0.71; <i>P</i><.001) and −0.99 kg/m2 (SE 0.29; <i>P</i>=.001), respectively. These differences persisted at 12 months. However, in the analyses that did not involve ITT, program completers achieved a significant weight loss of 5.6% (SE 0.81; <i>P</i><.001) at 6 months, maintaining 4.7% (SE 0.88; <i>P</i><.001) of their weight loss at 12 months. The control group lost −0.15% at 6 months (SE 0.64; <i>P</i>=.85) and gained 0.33% (SE 0.70; <i>P</i>=.63) at 12 months. Those randomized to the intervention group who did not start the program had no meaningful weight or HbA<sub>1c</sub> level change, similar to the control group. At 1 year, the intervention group showed a 0.23% reduction in HbA<sub>1c</sub> levels; those who completed the intervention showed a 0.28% reduction. Those assigned to the control group had a 0.16% reduction in HbA<sub>1c</sub> levels. This novel mobile-delivered DPP achieved significant weight loss reductions for up to 1 year compared with usual care. This type of intervention reduces the risk of overt diabetes without the added barriers of in-person interventions. ClinicalTrials.gov NCT03865342; https://clinicaltrials.gov/ct2/show/NCT03865342
DISCUSSION (CONCLUSION): This evidence-based CPG on osteoporosis is an important tool for the decisionmaking process of Spanish National Healthcare System professionals. TARGET AUDIENCE(S): 1. Clinical researcher 2. Evidence synthesizer, developer of systematic reviews or meta-analyses 3. Guideline developer 4. Developer of guideline-based products 5. Health care policy analyst/policy-maker
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